Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine.

Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.

FordNet: Recommending traditional Chinese medicine formula via deep neural network integrating phenotype and molecule.

Traditional Chinese medicine (TCM) formula is widely used for thousands of years in clinical practice. With the development of artificial intelligence, deep learning models may help doctors prescribe reasonable formulas. Meanwhile, current studies of formula recommendation only focus on the observable clinical symptoms and lack of molecular information. Here, inspired by the theory of TCM network pharmacology, we propose an intelligent formula recommendation system based on deep learning (FordNet), fusing the information of phenotype and molecule. We collected more than 20,000 electronic health records from TCM Master Li Jiren's experience from 2013 to March 2020. In the FordNet system, the feature of diagnosis description is extracted by convolution neural network and the feature of TCM formula is extracted by network embedding, which fusing the molecular information. A hierarchical sampling strategy for data augmentation is designed to effectively learn training samples. Based on the expanded samples, a deep neural network based quantitative optimization model is developed for TCM formula recommendation. FordNet performs significantly better than baseline methods (hit ratio of top 10 improved by 46.9% compared with the best baseline random forest method). Moreover, the molecular information helps FordNet improve 17.3% hit ratio compared with the model using only macro information. Clinical evaluation shows that FordNet can well learn the effective experience of TCM Master and obtain excellent recommendation results. Our study, for the first time, proposes an intelligent recommendation system for TCM formula integrating phenotype and molecule information, which has potential to improve clinical diagnosis and treatment, and promote the shift of TCM research pattern from "experience based, macro" to "data based, macro-micro combined" as well as the development of TCM network pharmacology.

Exploring the effect of Weifuchun capsule on the toll-like receptor pathway mediated HES6 and immune regulation against chronic atrophic gastritis.

ETHNOPHARMACOLOGICAL RELEVANCE: Weifuchun capsule (WFC) is a traditional Chinese patent medicine for chronic atrophic gastritis (CAG) in clinic. However, the mechanism of action of WFC for CAG still remains unclear due to its complex composition. AIM OF THE STUDY: The study was projected to uncover the mechanism of action of WFC and the corresponding pharmacodynamic substance of WFC against CAG as well as providing a standard example for the research of traditional Chinese medicine (TCM) from the perspective of the network and the system. MATERIALS AND METHODS: We identified the compounds of WFC through LC-MS/MS analysis and performed a systematic network targets analysis for WFC in the treatment of CAG which thoroughly described the mechanism of action of WFC for CAG. Based on analysis integrating omics data and algorithms, we focused on the specific immune regulatory role of WFC in the treatment of CAG, especially on a hub pathway, Toll-like receptor signaling pathway and thus deciphered the role of WFC in immune regulation, anti-inflammation and mediation of HES6. In experiments part, MNNG-GES-1-cell line and rat models were used to validate our findings. RESULTS: In this study, compounds of WFC are identified through LC‒MS/MS and network target analysis is performed to dissect the specific immunoregulatory effect as well as mediation of HES6, a newly discovered biomolecule related to gastritis carcinoma progression, of WFC on CAG through the Toll-like receptor signaling pathway. Based on cell line and rat models, we verify the mechanism of action of WFC for CAG in inhibiting inflammatory cytokines, regulating immune cells like T cells and macrophages, related genes including TLR2 and CD14. It is also validated that WFC inhibits the expression of HES6 (P < 0.05). CONCLUSION: Based on the combination of computational strategy and experiments, our study offers a comprehensive analysis to reveal the role of WFC in regulating immune response, inhibiting inflammation in the treatment of CAG, and provides a standard example for the research of TCM from the perspective of the network and the system.

An optimal medicinal and edible Chinese herbal formula attenuates particulate matter-induced lung injury through its anti-oxidative, anti-inflammatory and anti-apoptosis activities.

Objective: Identifying novel strategies to prevent particulate matter (PM)-induced lung injury is crucial for the reduction of the morbidity of chronic respiratory diseases. The combined intervention represented by herbal formulae for simultaneously targeting multiple pathological processes can provide a more beneficial effect than the single intervention. The aim of this paper is therefore to design a safe and effective medicinal and edible Chinese herbs (MECHs) formula against PM-induced lung injury. Methods: PM-induced oxidative stress, inflammatory response and apoptosis A549 cell model were used to screen anti-oxidant, anti-inflammatory and anti-apoptotic MECHs, respectively. A network pharmacology method was utilized to rationally design a novel herbal formula. Ultra performance liquid chromatography-mass spectrometer was utilized to assess the quality control of MECHs formula. The excretion of magnetic iron oxide nanospheres of the MECHs formula was estimated in zebrafish. The MECH formula against PM-induced lung injury was investigated with mice experiments. Results: Five selected herbs were rationally designed to form a new MECH formula, including Citri Exocarpium Rubrum (Juhong), Lablab Semen Album (Baibiandou), Atractylodis Macrocephalae Rhizoma (Baizhu), Mori Folium (Sangye) and Polygonati Odorati Rhizoma (Yuzhu). The formula effectively promoted the magnetic iron oxide nanospheres excretion in zebrafish. The mid/high dose formula significantly prevented PM-induced lung damage in mice by enhancing the activity of SOD and GSH-Px, reducing the MDA and ROS level and attenuating the upregulation of pro-inflammatory cytokine (IL-6, IL-8, IL-1ß and TNF-α), down regulating the protein expression of NF-κB, STAT3 and Caspase-3. Conclusion: Our findings suggest that the effective MECHs formula will become a novel strategy for preventing PM-induced lung injury and provide a paradigm for the development of functional foods using MECHs.

Multi-omics analysis to screen potential therapeutic biomarkers for anti-cancer compounds.

Discover potential biomarkers of the response for anti-cancer therapies, including traditional Chinese medicine (TCM), is a critical but much different task in the field of cancer research. Based on accumulated data and sophisticated methods, multi-omics analysis provides a feasible strategy for the discovery of potential therapeutic biomarkers. Here, we screened the potential therapeutic biomarkers for anti-cancer compounds in TCM through multi-omics data analysis. Firstly, compounds in TCM were collected from the public databases. Then, the molecules that those compounds can intervene on cell lines were carefully filtered out from existing drug bioactivity datasets. Finally, multi-omics analysis including gene mutation analysis, differential expression gene analysis, copy number variation analysis and clinical survival analysis for pan-cancer were conducted to screen potential therapeutic biomarkers for compounds in TCM. 13 molecules of compounds in TCM namely ERBB2, MYC, FLT4, TEK, GLI1, TOP2A, PDE10A, SLC6A3, GPR55, TERT, EGFR, KCNA3 and HDAC4 are differentially expressed, high frequently mutated, obtain high copy number variation rate and also significant in survival, are considered as the potential therapeutic biomarkers.

Corrigendum to "Multi-omics analysis to screen potential therapeutic biomarkers for anti-cancer compounds" [Heliyon 8 (9), (September 2022) e09616].

[This corrects the article DOI: 10.1016/j.heliyon.2022.e09616.].

Network pharmacology to unveil the mechanism of Moluodan in the treatment of chronic atrophic gastritis.

BACKGROUND: Moluodan (MLD) is a traditional Chinese patent medicine for the treatment of chronic atrophic gastritis (CAG). However, the mechanism of action (MoA) of MLD for treating CAG still remain unclear. PURPOSE: Elucidate the MoA of MLD for treating CAG based on network pharmacology. STUDY DESIGN: Integrate computational prediction and experimental validation based on network pharmacology. METHODS: Computationally, compounds of MLD were scanned by LC-MS/MS and the target profiles of compounds were identified based on network-based target prediction method. Compounds in MLD were compared with western drugs used for gastritis by hierarchical clustering of target profile. Key biological functional modules of MLD were analyzed, and herb-biological functional module network was constructed to elucidate combinatorial rules of MLD herbs for CAG. Experimentally, MLD's effect on different biological functional modules were validated from both phenotypic level and molecular level in 1- Methyl-3-nitro-1-nitrosoguanidine (MNNG)-induced GES-1 cells. RESULTS: Computational results show that the target profiles of compounds in MLD can cover most of the biomolecules reported in literature. The MoA of MLD can cover most types of MoA of western drugs for CAG. The treatment of CAG by MLD involved the regulation of various biological functional modules, e.g., inflammation/immune, cell proliferation, cell apoptosis, cell differentiation, digestion and metabolism. Experimental results show that MLD can inhibit cell proliferation, promote cell apoptosis and differentiation, reduce the inflammation level and promote lipid droplet accumulation in MNNG-induced GES-1 cells. CONCLUSION: The network pharmacology framework integrating computational prediction and experimental validation provides a novel way for exploring the MoA of MLD.

Network pharmacology to explore the anti-inflammatory mechanism of Xuebijing in the treatment of sepsis.

BACKGROUND: Xuebijing (XBJ) is a traditional Chinese patent medicine for sepsis. However, the mechanism of action (MoA) of XBJ on sepsis remain unclear. PURPOSE: Elucidate the MoA of XBJ for treating sepsis based on network pharmacology. STUDY DESIGN: Integrate computational prediction, experimental validation and literature reported clinical results analysis based on network pharmacology. METHODS: Computationally, representative compounds of XBJ were characterized by LC-MS/MS and the target profiles of each compound were identified using network-based method. Compounds from XBJ were compared with FDA approved drugs or experimental agents for sepsis by hierarchical clustering of target profile. Key biological functional modules of XBJ for treating sepsis were identified by enrichment analysis. Differential expressed analysis for each biological functional module was conducted from sepsis related public omics datasets. Herb-biological functional module network was constructed to reveal part of the traditional combinatorial rules of herbs for modules. Experimentally, the action of XBJ compounds on genes in biological functional module was validated by detecting quantitative transcriptional profiling and sepsis animal model. Clinically, combined with the clinical results recorded in literature, computational and experimental results were used to interpret the anti-inflammatory effect of XBJ for treating sepsis. RESULTS: The target profiles of compound cover most of the compound's related biomolecules reported in literature, which can characterize the comprehensive function of compound. XBJ has similar pharmacological effect as FDA approved drugs or experimental agents. Four key biological functional modules including inflammation, immune, cell apoptosis and coagulation of XBJ for treating sepsis were identified. Cell line experimental results show that part of ingredients in XBJ regulate the expression of genes in inflammation modules as predicted. Animal experiments show that compounds from XBJ could reduce the expression level of IL-1ß. Combined with literature reported clinical results, XBJ was found to exert anti-inflammatory effect through regulating the NF-kappa B signaling pathway. CONCLUSION: The network pharmacology framework integrating computational prediction, experimental validation and literature reported clinical results analysis provides a novel approach for analyzing MoA of XBJ for treating sepsis.

The nudge strategies for weight loss in adults with obesity and overweight: A systematic review and meta-analysis.

Obesity and overweight conditions have become major health challenges worldwide. The exploration of effective weight loss strategies is essential. Nudges are currently advancing approaches that represent a new and better method for changing the behaviors of people. However, the effectiveness of nudge interventions on weight loss in overweight people who may be obese has not been synthesized in a systematic manner. In this study, a systematic literature search was performed. Only randomized controlled trials (RCTs) were considered. Weighted mean differences (WMDs) and 95% confidence intervals (CIs) were calculated as summary statistics. In total, 25 RCTs involving a population of 5,929 individuals were included. Significant effects of the nudge strategy on weight loss (WMD: -0.96 kg, 95% CI: -1.49 to -0.43), body mass index (WMD: -0.3 kg/m2, 95% CI: -0.41 to -0.19) and waist circumference (WMD: -0.75 cm, 95% CI: -1.23 to -0.27) were observed. The subgroup analysis showed that the reduction in body weight associated with nudge interventions was significant in younger and more obese people. Moreover, the effect of nudge intervention on weight loss weakened over time. Overall, the nudge strategy can promote changes in weight loss, body mass index and waist circumference of adults, albeit at a mild magnitude and in particular types of individuals. Nudge strategies can be recommended to clinical practitioners and policy-makers to promote obesity management.